This application relates to the detection and classification of vehicles travelling on a road.
In modern road networks it is often necessary to determine the types of vehicles on a given road for a number of reasons. It is particularly useful at toll points to be able to determine automatically the type of vehicle approaching the barrier so that an appropriate amount can be charged to the driver according to the type of vehicle in question. A further application for the detection and classification of vehicles is in traffic monitoring systems, where it is useful to the operator of the road network to be able to determine the levels of traffic and the vehicle composition of the traffic in order to make strategic decisions relating to the operation of the roads.
Conventional systems used for this purpose often rely on either inductive sensors that count the number of wheel axles present on a vehicle, or utilise cameras alongside image processing techniques to classify vehicles. However, these systems give rise to a number of problems which the present invention seeks to address.
A typical inductive system comprises an inductive loop that operates using induction to detect the wheel axles as they pass the loop. Such systems however are prone to issues when multiple vehicles pass the sensor in quick succession, as they cannot distinguish between individual vehicles in bumper-to-bumper traffic, instead often detecting very long singular vehicles. Furthermore, only vehicles larger than a particular size can be detected, making it difficult to detect and classify bicycles, scooters and motorcycles, and false positive detections are not uncommon.
Systems that utilise optical techniques such as laser sensors or cameras have difficulty when visibility is poor, which is often the case in non-ideal weather conditions such as rain, snow and fog. However, hot weather can also be a problem as fumes from the road surface (normally made of asphalt) can dramatically hinder the performance of such optical systems. Cameras can often suffer problems with occlusion whereby, depending on the placement of the camera and the relative positions of the vehicles, a first vehicle may obstruct the view of a second vehicle, preventing the proper detection and classification of the vehicles. Laser based systems often struggle to differentiate between a fast long vehicle and a slower shorter vehicle. Furthermore, even with decreasing costs relating to optical devices in recent years, the physical implementations of these systems can be complex and expensive.
Both inductive loop and laser based systems are also usually calibrated or optimised for a particular range of speeds and require the vehicles to remain in a particular lane whilst being detected. However, the Applicant has appreciated that it would be advantageous to be able to detect and classify vehicles driving at any speed, in any driving pattern, in any prevailing weather conditions.